code: def get_state_dict(self):
      stream_ids = [
          worker.state_stream.remote() for worker in self.remote_workers
      ]
      # get the first task id that finished executing.
      [stream_id], stream_ids = ray.wait(
          stream_ids, num_returns=1, timeout=None
      )
      byte_obj = ray.get(stream_id)
      _buffer = io.BytesIO(byte_obj)
      state_dict = torch.load(_buffer, map_location="cpu")
      return state_dict
  
imports: import io
  import types
  import logging
  import numbers
  import torch
  import numpy
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.pytorch.torch_runner import TorchRunner
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.ray import RayContext
  import ray
  from ray.exceptions import RayActorError
label_desc: Use image node_id 10 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj.get_state_dict() and returns: state_dict